System, method and computer program for forecasting residual values of a durable good over time

ABSTRACT

Systems, methods and computer program products for forecasting future values of an item, where an initial value for the item is determined, and then a baseline forecast for a future reference period is computed based on factors that include microeconomic data which is specific to an industry of the item and macroeconomic data which is non-specific to the industry of the item. The forecast may also be adjusted based on data for a set of competitive items. The forecast for the item is stored and is then made available to clients that can access the forecast to determine the expected future value of the item at some point in the future.

CROSS-REFERENCE TO RELATED APPLICATION(S)

This is a conversion of and claims a benefit of priority from U.S. Provisional Application No. 61/683,552, filed Aug. 15, 2012, entitled “SYSTEM, METHOD AND COMPUTER PROGRAM FOR FORECASTING RESIDUAL VALUES OF A DURABLE GOOD OVER TIME,” which is hereby incorporated by reference as if set forth herein in its entirety.

TECHNICAL FIELD

This disclosure relates generally to forecasting future market value of durable goods, and more particularly to systems, methods and computer program products for forecasting the value of an item using microeconomic, macroeconomic, and competitive set information and updating the forecast value at predetermined time intervals.

BACKGROUND OF THE RELATED ART

The market value of an item is known at the time that it is sold to a consumer. After this initial transaction, however, the value of the item will decline. The amount by which the value decreases may depend upon many factors, such as the amount of time that has passed since the original sale, the amount of wear experienced by the item, and so on.

Because of the difficulty of determining these factors with any certainty, the value of an item after its initial sale is conventionally determined by resale values of the item. For instance, the value of a two-year-old automobile is determined by examining the prices for which similarly equipped automobiles of the same make, model and year have actually sold. While some adjustments may be made to these values (e.g., for vehicle mileage above or below some average range), determination of the automobile's value generally relies on past resale prices of the same vehicle.

Since these conventional methods of determining the value of an item are relatively simplistic and take into account only backward-looking data (e.g., past sales of the item), they are not as accurate as may be desired. For instance, an automobile leasing company may need to know the future value of the automobiles that it owns in order to obtain financing for expansion or other business transactions. It would therefore be desirable to provide improved methods for determining the future value of such items.

SUMMARY OF THE DISCLOSURE

This disclosure is directed to systems, methods and computer program products for forecasting future values of an item that solve one or more of the problems discussed above. One particular embodiment is a system for forecasting future values of an item, where the system includes a server computer coupled to a network and a local data storage device coupled to the server computer. The server computer is configured to receive data from one or more data sources external to the system. The server computer determines an initial value for an item, and then determines forecasted future values for the item which are adjusted based on microeconomic data which is specific to an industry of the item and macroeconomic data which is non-specific to the industry of the item. The server computer stores the forecasted future values in the local data storage device and enables access by a user to the forecasted future values. The server computer can modify the forecasted future values based on editorial input provided by the user and store the modified values in the local data storage device. The server computer can enable access by a client device to the modified forecasted future values through the network.

The server computer may be configured to receive and store competitive set data, and to modify the one or more future values based on the competitive set data. The server computer may have one or more crawlers that are configured to query the data sources via the network and to obtain data responsive to the queries. The server computer may be configured to scrub the data received from the data sources, and may identify and remove erroneous data. The server computer may be configured to determine one or more update intervals, to update the future values at the determined intervals, and to store the updated future values. The system may include a workbench that provides a user interface through which a user can access the future values, where the server modifies the future values based on input provided by the user through the workbench.

An alternative embodiment comprises a method for forecasting future values of an item. The method includes receiving data from one or more data sources via a network and determining an initial value for an item based on the received data. One or more future values are then determined for the item, where the future values are adjusted based on microeconomic data which is specific to the item's industry and macroeconomic data which is non-specific to the industry. The future values are then stored in a data storage device, and access to the future values by a user is enabled. The future values may be modified based on editorial input provided by a user. The modified future values may be stored in the data storage device, and access by a client device to the modified future values may be enabled.

Another alternative embodiment comprises a computer program product including a non-transitory computer-readable storage medium that stores computer instructions. The instructions are translatable by a processor to perform a method. The method may include receiving data from one or more data sources via a network and determining an initial value for an item based on the received data. One or more future values are then determined for the item, where the future values are adjusted based on microeconomic data which is specific to the item's industry and macroeconomic data which is non-specific to the industry. The future values are then stored in a data storage device, and access to the future values by a user is enabled. The future values may be modified based on editorial input provided by a user. The modified future values may be stored in the data storage device, and access by a client device to the modified future values may be enabled.

Numerous other embodiments are also possible.

BRIEF DESCRIPTION OF THE DRAWINGS

Other objects and advantages of the invention may become apparent upon reading the following detailed description and upon reference to the accompanying drawings.

FIG. 1 is a diagram illustrating the structure of an exemplary system in accordance with one embodiment.

FIG. 2 is a diagram illustrating the collection of different types of data that are collected by the system of one embodiment.

FIG. 3 is a diagram illustrating the collection of data from different data sources in the system of one embodiment.

FIG. 4 is a flow diagram illustrating the acquisition of data used in the generation of a forecast for an item in one embodiment.

FIG. 5 is a flow diagram illustrating the generation of a baseline forecast in one embodiment.

FIG. 6 is a flow diagram illustrating the adjustment of a baseline curve in one embodiment.

FIG. 7 is a flow diagram illustrating the adjustment of a residual curve by a client in one embodiment.

FIG. 8 is a diagram illustrating an exemplary baseline forecast or residual curve.

FIG. 9 is a diagram illustrating an exemplary residual curve adjustment in one embodiment.

While the invention is subject to various modifications and alternative forms, specific embodiments thereof are shown by way of example in the drawings and the accompanying detailed description. It should be understood, however, that the drawings and detailed description are not intended to limit the invention to the particular embodiment which is described. This disclosure is instead intended to cover all modifications, equivalents and alternatives falling within the scope of the present invention as defined by the appended claims. Further, the drawings may not be to scale, and may exaggerate one or more components in order to facilitate an understanding of the various features described herein.

DETAILED DESCRIPTION OF EXEMPLARY EMBODIMENTS

One or more embodiments of the invention are described below. It should be noted that these and any other embodiments described below are exemplary and are intended to be illustrative of the invention rather than limiting.

For the purposes of this disclosure, the term “item” may be used to refer to a durable good, product, or any item that has a known value at the time it was first sold and that has a different resale value over time thereafter. Examples of an item may include a vehicle, a real estate property, etc.

For a durable good or product (an “item”), the resale value of the item may be affected by various factors such as time, the availability of same or similar items, the geographical location where the item physically resides, demand in for the item in the resale market and/or industry, the purchasing power of the target buyers, and so on. An ability to determine the amount by which the item will change (e.g., devalue) over time, and thereby forecast the resale or residual value of the item can provide a better understanding of a company's assets and can allow the company to make better business decisions.

Embodiments disclosed herein provide a methodology for forecasting residual values of an item in two time periods and determining changes in value in a valuation metric. By estimating the changes in value for successive future time intervals, a function can be constructed to capture the estimated relationship between time and the item's value. Implementing the methodology, embodiments provide a model which can predict the residual value of an item at a future time point for any time period. In one embodiment, the current market value of an item at the beginning of an estimation period is known and can be used as a baseline against which future values are computed. The farther away in time a forecast is relative to the baseline, the more uncertainty will exist. Thus, the forecasting error will grow as the width of the time interval increases.

Taking this uncertainty into consideration, embodiments utilize different types of variables to aid in forecasting residual values of an item over time. Example types of forecasting variables include, but are not limited to, modifications to the items, locality of the items, depreciation of the items, microeconomic factors, macroeconomic factors, and sets of competitive items. These factors will be discussed in more detail below.

Referring to FIG. 1, a diagram illustrating the structure of an exemplary system in accordance with one embodiment is shown. FIG. 1 is a simplified diagrammatic representation of a network architecture 100 in which this embodiment is implemented. For purposes of clarity, a single client computer, a single server computer, and a single data source are shown in the figure. The client and server computers and data source represent an exemplary hardware configuration of data processing systems that are capable of bi-directionally communicating with each other over a public network such as the Internet. Those skilled in the art will appreciate that enterprise computing environment 130 may comprise multiple server computers, and multiple client computers and data sources may be bi-directionally coupled to enterprise computing environment 130 over network 120.

Client computer 110 can include central processing unit (“CPU”) 111, read-only memory (“ROM”) 113, random access memory (“RAM”) 115, hard drive (“HD”) or storage memory 117, and input/output device(s) (“I/O”) 119. I/O 119 can include a keyboard, monitor, printer, and/or electronic pointing device. Example of I/O 119 may include mouse, trackball, stylist, or the like. Client computer 110 can include a desktop computer, a laptop computer, a personal digital assistant, a cellular phone, or nearly any device capable of communicating over a network. Server computer 140 may have similar hardware components including CPU 141, ROM 143, RAM 145, HD 147, and I/O 149. Data source 160 may be a server computer having hardware components similar to those of client computer 110 and server computer 130, or it may be a data storage device.

Each computer shown in FIG. 1 is an example of a data processing system. ROM 113 and 143, RAM 115 and 145, HD 117 and 147, and database 150 can include media that can be read by CPU 111 and/or 141. Therefore, these types of computer memories include computer-readable storage media. These memories may be internal or external to computers 110 and/or 140.

Portions of the methods described herein may be implemented in suitable software code that may reside within ROM 143, RAM 145, HD 147, database 150, or a combination thereof. In some embodiments, computer instructions implementing an embodiment disclosed herein may be stored on a direct access storage device (DASD) array, magnetic tape, floppy diskette, optical storage device, or other appropriate computer-readable storage medium or storage device. A computer program product implementing an embodiment disclosed herein may therefore comprise one or more computer-readable storage media storing computer instructions translatable by CPU 141 to perform an embodiment of a method disclosed herein.

In an illustrative embodiment, the computer instructions may be lines of compiled C++, Java, or other language code. Other architectures may be used. For example, the functions of server computer 140 may be distributed and performed by multiple computers in enterprise computing environment 130. Accordingly, each of the computer-readable storage media storing computer instructions implementing an embodiment disclosed herein may reside on or accessible by one or more computers in enterprise computing environment 130.

As noted above, the present systems and methods utilize not only depreciation and other conventionally employed information to determine the future value of an item, but also microeconomic information, macroeconomic information and information on sets of competitive items. Referring to FIGS. 2 and 3, a pair of diagrams illustrating the collection of this information is shown. FIG. 2 illustrates the different types of data that are collected by the present system, while FIG. 3 illustrates the different sources from which the data is typically collected.

As depicted in FIG. 2, server 140 collects data types 161-166. Data types 161-163 represent more conventional information that is collected by the server. Data types 161-163 represent information that is related to past sales of similar items. The past sales indicate the amount of depreciation that the similar items have experienced from their original value to resale, and also indicate the effects of modifications and locality on resale value. Data sources 164-166, on the other hand, represent types of information that are not conventionally collected for use in determining the future value of an item. These data sources represent information that indicates how the value of the item will be impacted in the future, rather than how it was affected in the past.

Modifications 161 reflect any changes to an item i that may affect its value at any time point. Examples of modifications (M_(i)) include options added to the item in prior periods, different configurations/styles of the item, or other features which may distinguish one item from another that is produced by the same manufacturer.

Locality 162 represents valuation differences in an industry (p) that vary geographically. Examples of Locality (L_(p)) would include adjustments to equalize sales of the essentially identical items made in different locations, allowing valuation to be conducted, for instance, at both the national and state/province levels.

Depreciation 163 represents the natural change in value that occurs as the item i is used over time. Depreciation (D_(i)) can be determined from past sales of the item.

Microeconomic information 164 is specific to the industry p of the item i (i∈p). For example, microeconomic factors (G_(p)) may include supply and/or demand in the industry, industry trends, seasonality and/or volatility of the item, or information about a company that is in the industry.

Macroeconomic information 165 is non-specific to the item and its industry. Macroeconomic factors (F) may relate to the overall economy, rather than to the specific industry with which the item i is associated (e.g., the real estate or automotive industries). Examples of macroeconomic information may include inflation, unemployment, and interest rates.

Competitive set information 166 relates to a set of items that compete with the item of interest. Competitive sets (C_(iU)) include all other items, j=1, . . . , J (i≠j), in the same industry p and in the competitive set U (i,j∈U∀j) which are reasonable substitutes for the item i being valued. Examples of competitive items j may include items produced by different manufacturers that share similarities with the item i being valued and also sales incentives applied to the item i being valued or its substitutes. Competitive set information may include such information as sales or recall information for competing items.

Referring to FIG. 3, typical sources for data types 161-166 are shown. Depreciation, modification and locality data types are commonly obtained from data sources that are associated with auctions or wholesalers (167) dealing in the item of interest. Microeconomic information and data on competitive sets associated with the item of interest are commonly obtained from industry databases (168). Macroeconomic information may be obtained from databases or other sources associated with national economics (169).

Referring to FIGS. 4-7, a set of flow diagrams is shown. The diagrams illustrate a method for forecasting the future value of an item and enabling access by a client to the forecast in accordance with one embodiment. The method may be implemented, for example, in a system such as the one shown in FIG. 1. It should be noted that the particular steps illustrated in the figures are exemplary, and the steps of alternative embodiments may vary from those shown in the figures.

Referring to FIG. 4, a flow diagram illustrating the acquisition of data used in the generation of a forecast for an item is shown. The server initially queries one or more data sources for information (405). The data sources may include both data storage units internal to the enterprise computing environment and data sources external to the enterprise computing environment. In one embodiment, the enterprise computing environment may have crawlers that query external data sources, searching for information relevant to the forecast. The server collects the data (410) and stores the data for further processing (415). The data that is collected is examined by the server (420) and processed to identify portions of the data that will be used to generate the forecast. The data may be “scrubbed” by the server (425) in order to provide a better basis for the forecast. The scrubbing process may involve various techniques to improve the quality of the data, such as identifying data that appears to be in error, removing outlying data points that substantially deviate from the remainder of the data, and so on. The data may also be filtered or examined to identify particular fields or types of data within the data that has been collected by the system. Still further, the system may transform all or part of the collected data into forms that are suitable for use by the system. After the desired data has been selected and scrubbed, if necessary, the modified data set is stored (430) in a local data storage device, from which it can be retrieved and used in the generation of the forecast.

Referring to FIG. 5, a flow diagram illustrating the generation of a baseline forecast is shown. The server first determines an initial value for the item of interest (505). This value is commonly determined based upon past sales of the item. The server then adjusts the value of the item to account for modifications to the item that may affect its value (510). For instance, if the item has been upgraded in some manner, its value may have increased. The server may also adjust the value to account for things such as excessive wear on the item. The server may further adjust the value of the item to account for locality (515). The locality of the item may impact its value for various reasons, such as differing demand in different areas, different levels of wear in the different locations, etc. Using this initial value of the item, the server then generates a time-based value for the item which takes into account the depreciation of the item, as well as various microeconomic and macroeconomic factors (520). The microeconomic and macroeconomic factors effectively increase or decrease the rate of depreciation, depending upon the specific information that is taken into account. The resulting forecast of the value of the item over time is stored in a local data storage device (525) as a baseline for distribution or further adjustment. An exemplary baseline forecast or residual curve 800 is illustrated in FIG. 8.

Referring to FIG. 6, a flow diagram illustrating the adjustment of a baseline curve is shown. After the baseline residual curve has been generated and stored in a local data storage device, a user of the enterprise computing environment can provide editorial input (605) that is used to revise the residual curve (610). The editorial input may be provided to account for any factors that were not accounted for in the generation of the baseline curve, or that have changed since the baseline curve was generated. The editorial input may also be provided to determine the potential affect of various factors on the residual curve. The editorial input may be provided through a workbench application that allows the user to see the results of the input. The residual curve that is revised according to the editorial input can then be made “live” (615). In other words, the revised residual curve can be stored or published to a location to which client access can be enabled. It may be desirable to periodically revise the residual curve. If it is time to do so (620) (e.g., if a predetermined interval has been reached), the user can provide additional editorial input (605) for generation of a newly revised residual curve (610), which can then be published for access by the client (615).

In one embodiment, the residual curve is updated at regular intervals. The updated residual curve can be stored in place of the previous baseline curve and used as the baseline for future use. When the residual curve is updated, several comparisons are made to ensure that the newly revised curve is reasonable. For example, the revised curve is compared to the previous curve to determine whether the values of the new curve differ from the previous curve by a substantial amount. If the difference is too great, this may indicate that the inputs to the revised curve are not realistic. The inputs may therefore be adjusted to bring the revised residual curve closer to the previous curve. In one embodiment, the residual curve is also adjusted based on the current values of items in a competitive set. For instance, the curve may be adjusted to bring the curve closer to the value of a closest competitive item, or to the average value of the set of competitive items. Referring to FIG. 9, an exemplary residual curve adjustment is shown. In this figure, the dotted line (900) represents the initial computation of the curve. Points A, B, and C represent the average residual values of the competitive set, the current market value of the best matching item in the competitive set, and the previous value of the item of interest, respectively. Taking these data points into account, the final revised residual curve is shown as line 910.

Referring to FIG. 7, a flow diagram illustrating the adjustment of a residual curve by a client is shown. After the baseline residual curve is revised by the editorial input, the server can enable access by the client to the revised curve (705). Customers can access the residual curve through the client in order to determine the value of the item at some point in the future. The client in this embodiment includes a workbench application that allows the customer to vary some of the factors that affect the residual curve and to view the resulting changes to the residual curve. The server receives input from the client's workbench application (710) and revises the residual curve according to the received input (715). The newly revised residual curve is then provided to the client (720) so that it can be viewed by the customer.

In one embodiment, the residual values of an item i (V_(i,n)) may be expressed in an equation below as a function of time t_(n) (n=0, . . . , T):

V _(i,n)=(V′ _(i,0) +M _(i,n))×(τ_(i,n) ×L _(p,n))×D _(j,n|0)+(F. _(,n|n−h) +G _(k,n|n−h))+C _(iU,n|n) * h=1, . . . , H

In the above equation, “F.” implies that the macroeconomic factors are taken over all p=1, . . . ,P and “n*” pertains to period t_(n)* defined as a reference period, t_(n)*, at which adjustments will be made to align values with other items in the competitive set. Further,

-   -   V′_(i,0) represents the market value at time t₀ before         modifications, reflecting the level of the base configuration of         item i at period t₀, prior to modifications and locality         adjustments,     -   V′_(i,n) reflects the level of the variable for the item i at         period t_(n),     -   L_(p,n) reflects the locality adjustment made at time t_(n) to         all items i_(n) for industry p (i∈p),     -   F._(,n|n−h) reflects the level of a macroeconomic (neither         industry- nor item-specific) variable at period t_(n), given the         historical information about that the variable in the last=1, .         . . , H periods (t_(n−1), t_(n−2), . . . , t_(n−H)),     -   G_(p,n|n−h) reflects the level of the microeconomic variable at         period t_(n) given the historical information for industry p         (i∈p) available about that variable in the last=1, . . . , H         periods (t_(n−1), t_(n−2), . . . , t_(n−H)),     -   D_(j,n|0) reflects the observed natural depreciation for another         item j in the same competitive set as item I at time period t₀,     -   C_(iU,n|n)* reflects a competitive set adjustment made to item i         based at time period t_(n)* based on an observed discrepancy         between V_(i,n) and the predicted values of all other items,         j=1, . . . , J (i≠j) in the competitive set U (i,j∈U∀j)         evaluated at some reference period, t_(n).

Following is an exemplary methodology that can be used to estimate the value of an item over time, thereby providing the resale value of the item that could be expected at future time points.

In this embodiment, a method for forecasting residual values of an item begins with determining a baseline value of an item. The baseline value of the item can be an unmodified value for the item that is obtained through direct observation of the current market values. If the baseline value cannot be determined or obtained directly for the item, a competitive set of similar and substitute items in the same industry as the item may be constructed. The item in the competitive set that is the most similar to the item of interest (the item being evaluated) can be selected as a substitute and its value can be used as the baseline value for the item. This baseline value reflects the market information across localities in which the item is available.

The method further includes determining, at an initial time point t₀, a reference period at which adjustments are made to align the baseline value of the item with values of other items in a competitive set of similar and substitute items in the same industry as the item. In one embodiment, this competitive set can be the same competitive set used in selecting a substitute item whose value is used as the baseline value for the item under evaluation. The reference period may be determined utilizing a set of constraints and/or considerations. Example constraints and considerations may include the expected lifespan for the item, the purpose for obtaining the residual value of the item, how often input data to the model (for predicting the residual value of the item at a future time point for any time period) is updated, etc. As an example, suppose the initial time point is Jul. 9, 2012 and input data to the model is updated on a monthly basis, the reference period could then be Jul. 9, 2012 to Aug. 9, 2012, Jul. 9, 2012 to Sep. 9, 2012, Jul. 9, 2012 to Oct. 9, 2012, etc. The reference period can be further constrained by the total expected lifetime of the item. For example, if the item is not expected to retain value after five years, then the reference period can be Jul. 9, 2012 to Jul. 9, 2017, or less (in one or more monthly temporal offsets as constrained by the update frequency of the input data to the model).

Once the reference period has been determined, a number of forecasts desired between the initial time point and the reference period can be determined. This time determines how often a forecast of the residual value of the item is to be generated. Starting from the initial time point, the time interval at which a forecast is to be generated can be the same as, or more than, the update frequency of the input data to the model. Following the above example in which the expected lifespan of the item is five years, if it is assumed that the reference period is two years, there can be, for example, 23 forecasts, each of which is generated at a fixed time interval of one month. If the time interval is selected to be six months, then four forecasts are generated.

With the time interval thus selected, the method further comprises determining a locality adjustment (L_(p)). If the value of the item does not vary by geographic region (the value of the item is the same in the industry across all localities at the initial time period), then no locality adjustment needs to be made. Otherwise, the baseline value of the item at the initial time point t₀ may be adjusted by computing a ratio between the average cost of items in the industry in a particular locality at a certain time point t_(n) and the local cost of items in the industry across all localities at the same time point t_(n). As an example, consumer price index can be utilized to determine the cost information on items in various industries relative to localities.

The exemplary method further comprises collecting or estimating incremental values of modifications (M_(i,n)) to the base value of the item of interest. These modifications can be of various types. One example type can be modifications that are both observable at a particular time t_(n) and are expected to retain some value in future time period(s) after the particular time t_(n). Another example type can be modifications that are not observable and/or not expected to retain value after the particular time t_(n).

With the collected or estimated incremental values of modifications (M_(i,n)), the method may further comprise adjusting the base value of the item to account for the modifications and locality adjustments to determine a locally-adjusted value of a modified item (BV_(i,n)) at the particular time t_(n).

The method may further comprise constructing competitive sets of similar and substitute items in the same industry. This construction may involve partitioning all items in the industry into distinct clusters based on a measure of similarity between all pairs of items in the industry. A full explanation of an example competitive set approach is provided in U.S. patent application Ser. No. 13/173,332, filed Jun. 30, 2011, entitled “SYSTEM, METHOD AND COMPUTER PROGRAM PRODUCT FOR PREDICTING ITEM PREFERENCE USING REVENUE-WEIGHTED COLLABORATIVE FILTER,” which is fully incorporated herein by reference. Other competitive set approaches may also be possible.

To account for macroeconomic factor(s), the method may further comprise collecting macroeconomic data, F._(,n|n−h), and either forecasting future levels or incorporating existing forecasts from other sources to determine {circumflex over (F)}_(.,n|n−h). Here, “F.” implies that the macroeconomic factors are taken over all industries and not specific to any particular industry p. “{circumflex over (F)}.” indicates that it is an estimated value.

To account for microeconomic factor(s), the method may further comprise collecting microeconomic data, G_(p,n|n−h), for the industry p in which the item i being evaluated is classified and either forecasting future levels or incorporating existing forecasts from other sources to determine Ĝ_(p,n|n−h). Here, each microeconomic factor is specific to the industry p. “Ĝ” indicates that it is an estimated value.

To account for the depreciation (D_(i,n|0)) that represents the natural change in value that occurs as the item i is used over time, the method may further comprise using standard accounting definitions as a basis for expressing the relationship between depreciation and time. For example, the natural change in value can represent a linear (straight line) depreciation. If there are no guidelines on building industry-specific depreciation factors or no depreciation information can be obtained or provided by another source, the depreciation factor from another item j in the same competitive set may be used as a substitute.

To compute a residual value for the item i ({circumflex over (V)}_(i,n)′) at the particular time t_(n) in one embodiment, the method may comprise solving the equation below using values constructed from the above-described steps.

{circumflex over (V)} _(i,n) ′=BV _(i,n) ×D _(i,n|0)+({circumflex over (F)} _(.,n|n−h) +Ĝ _(p,n|n−h)) where h=1, . . . , H

Although the initial value of the item i, V_(i,0), is known and remains unchanged, the forecasted residual value needn't also remain fixed over time. As new information becomes available, it is possible to employ that additional information to update the forecasted value. This new information may be reflected in the variable types listed above.

In one embodiment, for quality assurance (QA), the forecasted residual values thus determined may be compared with a set of reference values and adjusted by an adjustment value, C_(iU,n|n)* , that will minimize the weighted average error relative to the position implied by the reference points.

As an example, the approach for adjusting {circumflex over (V)}_(i,n)′ for QA purposes may include the following steps:

-   -   Gather residual values from other goods in the competitive set,         including, for example:         -   The average residual value at t*_(n) for the entire             competitive set U_(k).         -   The baseline value BV_(j,0) at t₀ for the good j in the same             competitive set that is most similar to good i.         -   The residual value of the good k that is a previous version             of good i (not a modification of good i, but the one that             was replaced in production by good i), if it exists.     -   Compute the adjustment value, C_(iU,n|n)*, that will minimizes         the weighted average error relative to the position implied by         the reference points as follows:

C _(iU,n|n)*=Δ[(αV _(U,n) +βBV _(j,0) +ΓV _(k,n))−V _(i,n)],

where α, β, Γ are assigned weights, α+β+Γ=1 and α, β, Γ>0, Δ is a weight depending on whether good i at t_(n) is completely new in the market (Δ=1) or established (Δ<1).

Adjusting {circumflex over (V)}_(i,n)′ by C_(iU,n|n)* produces the final forecasted residual value for the item i:

{circumflex over (V)} _(i,n) =BV _(i,n) ×D _(j,n|0)+({circumflex over (F)} _(n|n−h) +Ĝ _(k,n|n−h))+C _(iU,n|n) * h=1, . . . , H

Those skilled in the art will appreciate that embodiments disclosed herein can apply to all types of items that are not immediately consumed and that retain some non-negative value over time. The variable types disclosed herein and are intended to encompass the various components required to value an item in any industry p and the microeconomic (G_(p)), Locality (L_(p)), and competitive sets (C_(iU)) components will be specific to that industry class pertaining to the item i that is being valued as long as all other items or members, j=1, . . . , J, of the competitive set U are in the same industry, p, as item i.

Embodiments disclosed herein can provide many advantages. For example, knowledge of the future residual values can be used to:

-   -   a) Set leasing rates of an item which reflect the expected         change in valuation of the item between the beginning and ends         of a fixed lease period—a useful metric that can be used in the         rental industry.     -   b) Determine the amount at which an item can be resold at any         time period—a useful metric that can be used in investment         decisions such as real estate.     -   c) Provide information supporting the strategic planning         decisions made of the manufacturer of good i.     -   d) Determine if the change in value will be constant over time         intervals of the same length.

Example Implementation in Automotive Industry

The following describes an exemplary implementation in which the approach described above is adapted to be used to estimate the value of automobiles over time and thereby allows the resale values that could be expected at future time points to be determined. Residual values thus estimated can provide guidelines for pricing fixed-term vehicle leases which captures the expected change in value that will result in the time interval between the leased vehicle's acquisition at time t₀ and its disposition at time t_(d). This example implementation not only can provide the estimated residual value at disposition, V_(i,d), but can also forecast values at equally-spaced fixed points between t₀ and t_(d), thereby allowing construction of a residual curve that captures the relationship between vehicle value and time. Over time and as new information becomes available, this example implementation allows a company to update their forecasts to reflect changing values of exogenous macroeconomic and industry-specific microeconomic variables and vehicle-specific, endogenous variables (depreciation, competitive sets, and modifications). In this example implementation, the guidelines for production of the residual values include:

-   -   The residual values must be realistic and adjusted over the         vehicle's lifecycle to reflect the market, incentives and fleet         purchases.     -   To enable vehicle manufacturers to market their vehicles,         residual values must create clear, consistent messages across         all vehicles being valued.     -   To remain relevant and timely, the residual values must reflect         product enhancements, packaging/content adjustments, etc.     -   To provide utility to each manufacturer's ecosystem, the         residual values must encompass all phases of the automotive         sales cycle, including dealer engagement, manufacturer support,         cooperation on pricing, and off lease supply management.

In this example, the methodology described above is adapted to estimate residual values of cars and light trucks in the United States and Canada. Estimates are updated every two months to reflect new observed data, market conditions, and macroeconomic estimates. In this example, the latest Model Year (MY) Toyota Camry LE with automatic transmission (AT)—which sells at popular equipped Manufacturer Suggested Retail Price (MSRP) of $23,800 in California—will be used. This particular model has some historical used market value and residual value data available to estimate the future value of the vehicle. Furthermore, exogenous macro and micro-economic data as well as endogenous factors (depreciation rate and competitive knowledge) are readily available to construct the current residual value curve for this vehicle at any term (e.g., 12-month, 24-month, 36-month, . . . , 60-month and any term in between).

Step 1. Determine a baseline, unmodified value for the durable good, V′_(i,0), either through direct observation of the current market values. The 2010MY Camry LE AT baseline value for t₀ (set to June 2012, as an example) is V′_(i,0)=$13,640 and is based on observed current market value (CMV) derived from auction data. Roughly 990 auction records were available in June 2012 for the 2010MY Camry LE AT to create the CMV of $13,640 by applying statistical filters and other measures to cleanse the data. For the purpose of illustration and not of limitation, auction records may include such information as: Sale Date; National Automobile Dealers Association (NADA) Vehicle Identification Code; Make; Sub-make; Model Year; Series; Body Style; Diesel 4WD Identifier; NADA Region code; Sale Price; Mileage; Sale Type; Vehicle Identification Number (VIN); Vehicle Identifier (VID); etc.

Step 2. At time t_(n)=0, determine a reference period, t_(n)*, at which adjustments will be made to align values with other durable goods in the competitive set based on the following industry-level frequencies that constrain the choice of t_(n)*:

-   -   auction data is updated weekly yet also aggregated to monthly         numbers, while micro and macro-economic factors are updated         monthly;     -   forecasted terms go up to 72-month, t_(max) is greater than         72-month;     -   most common terms are 12, 24, 36, 48, and 60-month terms, mostly         36-month is used.

Because a 36-month alignment is commonly used in the automotive industry, a value of t_(n)*=36 months is used in this example for the reference period relative to the baseline.

Step 3. At time t_(n)=0, determine the constant width of time intervals Δ_((p,q)) at which forecasts will be generated. In this case, the selection of Δ_((p,q)) is determined by considering the following constraints;

-   -   It must be chosen such that (t_(n)*−t₀)/Δ_((p,q)) is a positive         integer where t_(n)*−t₀=36 months.     -   It must be greater than or equal to φ*=min_(r)(φ_(r))=weekly         since that is the frequency at which the macroeconomic data is         updated.

Given those constraints and a choice of t_(n)*=36, the Δ_((p,q))=2 months is used.

-   -   36-month term/2 month=18 >0.     -   Interval is greater than φ* (weekly data).

Step 4. Determine a locality adjustment, L_(p). If the base value of the goods in industry p to which durable good i is assigned varies by geographic region, then compute

$L_{p,n} = \frac{L_{p,n}^{\prime}(z)}{L_{p,n}^{\prime}(Z)}$

where L′_(p,n)(z) is the average cost of durable goods in industry p in locality z at time t_(n), and L′_(p,n)(Z) is the local cost of durable goods in industry p across all localities (z∈Z) at time t_(n). In this example, the residual value of the 2010MY Toyota Camry LE AT is being established for California, located in the z=“US West” region of the U.S. and where

${L_{west} = \frac{1.1}{1}},$

local adjustment for U.S. Western region is 110% of the average for all regions in the U.S.

Step 5. Collect or estimate incremental values of modifications, M_(i,n), to the base configuration of the durable good. In this example, the vehicle has navigation added as popularly equipped which retains a measurable and tangible value of $450 at 36-month. Thus, M_(Camry,36-month)=$450 for all regions; and M_(Camry,36-month)=($450×1.1) for U.S. Western region.

Step 6. Determine the locally-adjusted value of the modified good i at time t_(n) by adjusting the base configuration's value to account for modifications and locality adjustments.

BV _(i,n)=(V′ _(i,0) +M _(i,n))×(τ_(i,n) ×L _(p,n))=($13,640+$450)×1.1=$15,499

Step 7. Construct competitive sets, C_(iW,n), of similar and substitute durable goods in the same industry, p. This involves determining factors to compare to for each competitor and establish a matrix such as pricing (MSRP), engine and performance (horse power, mile per gallon, torque, displacement, etc.), exterior (curb weight, wheelbase, length, width, height, wheels size, etc.), interior (dimensions, features, air conditioning, entertainment system, seats, etc.) and safety.

Based on the factors above and the matrix analysis, for example, the 2010 Honda Accord LX AT has the most similarities to the 2010 Camry LE AT, followed by 2010 Nissan Altima 2.5.

Step 8. Collect macroeconomic data, F._(,n|n−h), and either forecast future levels or incorporate existing forecasts from other sources to determine {circumflex over (F)}_(.,n|n−h).

In this case, housing prices, real wage growth, and gas prices are collected at t₀ and forecasted for t_(n). For example, if housing price index is equal to 190 index points, real wage is 59 points, and average gas prices are $3.56 per gallon in t₀, the forecasts are 202 points for housing, 63 points for real wage, and $3.90 for gas price in t_(36-month). The various factors have coefficients which determined based on correlation to auction data and thus the impact on the forecasted values can be applied by using the coefficients (see equations described above). Hence, for example, based on the change in housing price index from currently 190 to 202 in 36-month, the impact on 36-month residual values is an incremental $200, from gas prices $170, and from wages $630. In this case, the total adjustment for macro-economic variables is $1,000.

Step 9. Collect microeconomic data, G_(p,n|n−h), for the industry in which the good being evaluated is classified and forecast future levels or incorporate existing forecasts from other sources to determine Ĝ_(p,n|n−h).

Micro-economic data, such as brand value, incentive spending, and rental fleet penetration, can be generated for t₀ and forecasted for t_(36-month). For example, current incentive spending for the Camry is $2,900, yet the forecast is expected to be at $2,750. Thus, applying the equations described above and based on the change in incentive spending from today to 36-month, the impact is an incremental $140, from rental fleet penetration −$130, brand value $50, used supply $75, lifecycle −$85 and market volatility $1,300. In this case, the total adjustment for micro-economic variables is $1,250.

Step 10. Determine the natural depreciation values, D_(i,n|o), for the durable good over time. Based on historic data, annual or 3-year depreciation can be constructed. In this example, the Camry LE AT is expected to depreciate 14% annually.

Step 11. With all the pieces assembled, forecasting the residual value for time t_(n)*=36 month for the 2010 Toyota Camry LE AT (with navigation, in California) can be done by substituting the values constructed in earlier steps into the equation:

$\begin{matrix} {{\hat{V}}_{i,n}^{\prime} = {{{BV}_{i,n} \times D_{i,{n0}}} + \left( {{\hat{F}}_{.{,{n{n - h}}}} + {\hat{G}}_{p,{n{n - h}}}} \right)}} \\ {= {{\left( {\left( {{{\$ 13},640} + {\$ 450}} \right) \times 1.1} \right) \times \left( {1 - 0.14} \right)^{3}} + \left( {{{\$ 1},000} - {{\$ 1},250}} \right)}} \\ {= {{{\$ 9},850} - {\$ 250}}} \\ {= {\$ 9600}} \end{matrix}$

Step 12. Perform quality assurance (QA). In this example, this involves computing the adjustment value, C_(iU,n|n)*, that will minimizes the weighted average error relative to the position implied by the reference points. The adjustment value in the case of the Camry LE AT is small (Δ<1), since it is an established good and plenty of history is available. The average residual value of entire competitive set is $9,200 and the following factors are taken into account:

-   -   i. Baseline of the closest competitor is $9,500     -   ii. 2009MY Toyota Camry LE is $10,000

Applying the equation described above, the adjustment value C_(iU,n|n)* is then equal to

0.25×((0.33×9,200+0.33×$9,500+0.33×$10,000)−$9,600)=−$32

Step 13. Adjust {circumflex over (V)}_(i,n)′ by C_(iU,n|n)* to determine the final forecasted value:

In this example, the final forecast for the 2010MY Toyota Camry LE AT for the Western region for time t_(n)*=36 month is $9,600−$32=$9,568.

These, and other, aspects of the disclosure and various features and advantageous details thereof are explained more fully with reference to the exemplary, and therefore non-limiting, embodiments illustrated and detailed in this disclosure. It should be understood, however, that the detailed description and the specific examples, while indicating the preferred embodiments, are given by way of illustration only and not by way of limitation. Descriptions of known programming techniques, computer software, hardware, operating platforms and protocols may be omitted so as not to unnecessarily obscure the disclosure in detail. Various substitutions, modifications, additions and/or rearrangements within the spirit and/or scope of the underlying inventive concept will become apparent to those skilled in the art from this disclosure.

Embodiments discussed herein can be implemented in a computer communicatively connected to a network (for example, the Internet), another computer, or in a standalone computer. As is known to those skilled in the art, a suitable computer can include a central processing unit (“CPU”), at least one read-only memory (“ROM”), at least one random access memory (“RAM”), at least one hard drive (“HD”), and one or more input/output (“I/O”) device(s). The I/O devices can include a keyboard, monitor, printer, electronic pointing device (for example, mouse, trackball, stylist, touch pad, etc.), or the like. In embodiments of the invention, the computer has access to at least one database over a network connection.

ROM, RAM, and HD are computer memories for storing computer-executable instructions executable by the CPU or capable of being complied or interpreted to be executable by the CPU. Suitable computer-executable instructions may reside on a computer readable medium (e.g., ROM, RAM, and/or HD), hardware circuitry or the like, or any combination thereof. Within this disclosure, the term “computer readable medium” or is not limited to ROM, RAM, and HD and can include any type of data storage medium that can be read by a processor. Examples of computer-readable storage media can include, but are not limited to, volatile and non-volatile computer memories and storage devices such as random access memories, read-only memories, hard drives, data cartridges, direct access storage device arrays, magnetic tapes, floppy diskettes, flash memory drives, optical data storage devices, compact-disc read-only memories, and other appropriate computer memories and data storage devices. Thus, a computer-readable medium may refer to a data cartridge, a data backup magnetic tape, a floppy diskette, a flash memory drive, an optical data storage drive, a CD-ROM, ROM, RAM, HD, or the like.

The processes described herein may be implemented in suitable computer-executable instructions that may reside on a computer readable medium (for example, a disk, CD-ROM, a memory, etc.). Alternatively, the computer-executable instructions may be stored as software code components on a direct access storage device array, magnetic tape, floppy diskette, optical storage device, or other appropriate computer-readable medium or storage device.

Any suitable programming language can be used to implement the routines, methods or programs of embodiments of the invention described herein, including C, C++, Java, JavaScript, HTML, or any other programming or scripting code, etc. Other software/hardware/network architectures may be used. For example, the functions of the disclosed embodiments may be implemented on one computer or shared/distributed among two or more computers in or across a network. Communications between computers implementing embodiments can be accomplished using any electronic, optical, radio frequency signals, or other suitable methods and tools of communication in compliance with known network protocols.

Different programming techniques can be employed such as procedural or object oriented. Any particular routine can execute on a single computer processing device or multiple computer processing devices, a single computer processor or multiple computer processors. Data may be stored in a single storage medium or distributed through multiple storage mediums, and may reside in a single database or multiple databases (or other data storage techniques). Although the steps, operations, or computations may be presented in a specific order, this order may be changed in different embodiments. In some embodiments, to the extent multiple steps are shown as sequential in this specification, some combination of such steps in alternative embodiments may be performed at the same time. The sequence of operations described herein can be interrupted, suspended, or otherwise controlled by another process, such as an operating system, kernel, etc. The routines can operate in an operating system environment or as stand-alone routines. Functions, routines, methods, steps and operations described herein can be performed in hardware, software embodied on hardware, firmware or any combination thereof.

Embodiments described herein can be implemented in the form of control logic in hardware or a combination of software and hardware. The control logic may be stored in an information storage medium, such as a computer-readable medium, as a plurality of instructions adapted to direct an information processing device to perform a set of steps disclosed in the various embodiments. Based on the disclosure and teachings provided herein, a person of ordinary skill in the art will appreciate other ways and/or methods to implement the invention.

It is also within the spirit and scope of the invention to implement in software programming or code an of the steps, operations, methods, routines or portions thereof described herein, where such software programming or code can be stored in a computer-readable medium and can be operated on by a processor to permit a computer to perform any of the steps, operations, methods, routines or portions thereof described herein. The invention may be implemented by using software programming or code in one or more general purpose digital computers, by using application specific integrated circuits, programmable logic devices, field programmable gate arrays, optical, chemical, biological, quantum or nanoengineered systems, components and mechanisms may be used. In general, the functions of the invention can be achieved by any means as is known in the art. For example, distributed, or networked systems, components and circuits can be used. In another example, communication or transfer (or otherwise moving from one place to another) of data may be wired, wireless, or by any other means.

A “computer-readable medium” may be any medium that can contain, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, system or device. The computer readable medium can be, by way of example only but not by limitation, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, system, device, propagation medium, or computer memory. Such computer-readable medium shall generally be machine readable and include software programming or code that can be human readable (e.g., source code) or machine readable (e.g., object code). Examples of non-transitory computer-readable media can include random access memories, read-only memories, hard drives, data cartridges, magnetic tapes, floppy diskettes, flash memory drives, optical data storage devices, compact-disc read-only memories, and other appropriate computer memories and data storage devices. In an illustrative embodiment, some or all of the software components may reside on a single server computer or on any combination of separate server computers. As one skilled in the art can appreciate, a computer program product implementing an embodiment disclosed herein may comprise one or more non-transitory computer readable media storing computer instructions translatable by one or more processors in a computing environment.

A “computer” or “processor” may include any hardware system, mechanism or component that processes data, signals or other information. A computer or processor can include a system with a general-purpose central processing unit, multiple processing units, dedicated circuitry for achieving functionality, or other systems. Processing need not be limited to a geographic location, or have temporal limitations. For example, a computer or processor can perform its functions in “real-time,” “offline,” in a “batch mode,” etc. Portions of processing can be performed at different times and at different locations, by different (or the same) processing systems.

As used herein, the terms “comprises,” “comprising,” “includes,” “including,” “has,” “having,” or any other variation thereof, are intended to cover a non-exclusive inclusion. For example, a process, product, article, or apparatus that comprises a list of elements is not necessarily limited only those elements but may include other elements not expressly listed or inherent to such process, process, article, or apparatus.

Furthermore, the term “or” as used herein is generally intended to mean “and/or” unless otherwise indicated. For example, a condition A or B is satisfied by any one of the following: A is true (or present) and B is false (or not present), A is false (or not present) and B is true (or present), and both A and B are true (or present). As used herein, including the accompanying appendices, a term preceded by “a” or “an” (and “the” when antecedent basis is “a” or “an”) includes both singular and plural of such term, unless clearly indicated otherwise (i.e., that the reference “a” or “an” clearly indicates only the singular or only the plural). Also, as used in the description herein and in the accompanying appendices, the meaning of “in” includes “in” and “on” unless the context clearly dictates otherwise.

Although the foregoing specification describes specific embodiments, numerous changes in the details of the embodiments disclosed herein and additional embodiments will be apparent to, and may be made by, persons of ordinary skill in the art having reference to this disclosure. In this context, the specification and figures are to be regarded in an illustrative rather than a restrictive sense, and all such modifications are intended to be included within the scope of this disclosure. Accordingly, the scope of this disclosure should be determined by the following claims and their legal equivalents. 

What is claimed is:
 1. A system for forecasting future values of an item, the system comprising: a server computer coupled to a network; and a local data storage device coupled to the server computer; wherein the server computer is configured to: receive data from one or more data sources external to the system; determine an initial value for an item; determine one or more future values for the item, wherein the one or more future values are adjusted based on microeconomic data which is specific to an industry of the item and macroeconomic data which is non-specific to the industry of the item; store the one or more future values in the local data storage device; enable access to the one or more future values by a user; modify the one or more future values based on editorial input provided by the user; store the one or more modified future values in the local data storage device; and enable access by a client device to the one or more modified future values through the network.
 2. The system of claim 1, wherein the server computer is configured to receive and store competitive set data, and to modify the one or more future values based on the competitive set data.
 3. The system of claim 1, wherein the server computer has one or more crawlers, wherein the crawlers are configured to query at least one of the one or more data sources external to the system via the network and to obtain data from the queried data sources responsive to the queries.
 4. The system of claim 1, wherein the server computer is further configured to scrub the data received from the data sources external to the system.
 5. The system of claim 4, wherein the server computer is configured to identify erroneous data and to remove the identified erroneous data from the received data.
 6. The system of claim 1, wherein the server computer is further configured to determine one or more update intervals, to update the future values at the determined intervals, and to store the updated future values.
 7. The system of claim 1, further comprising a workbench configured for providing a user interface which enables a user to access the one or more future values, wherein the server computer modifies the one or more future values based on input provided by the user through the workbench.
 8. A method for forecasting future values of an item, the method comprising: receiving, by a server computer, data from one or more data sources communicatively connected to the server computer via a network; determining, by the server computer, an initial value for an item; determining, by the server computer, one or more future values for the item, wherein the one or more future values are adjusted based on microeconomic data which is specific to an industry of the item and macroeconomic data which is non-specific to the industry of the item; storing, by the server computer, the one or more future values in a data storage device; enabling, by the server computer, access to the one or more future values by a user; modifying, by the server computer, the one or more future values based on editorial input provided by the user; storing, by the server computer, the one or more modified future values in the data storage device; and the server computer enabling access by a client device to the one or more modified future values.
 9. The method of claim 8, further comprising: receiving and storing competitive set data; and modifying the one or more future values based on the competitive set data.
 10. The method of claim 8, further comprising: querying at least one of the one or more data sources using a crawler; and obtaining data from the queried data sources responsive to the queries.
 11. The method of claim 8, further comprising: scrubbing the data received from the data sources.
 12. The method of claim 11, further comprising: identifying erroneous data; and removing the identified erroneous data from the received data.
 13. The method of claim 8, further comprising: determining one or more update intervals; updating the future values at the determined intervals; and storing the updated future values.
 14. The method of claim 8, further comprising: enabling a user to access the one or more future values through a workbench module; and modifying the one or more future values based on input provided by the user through the workbench module.
 15. A computer program product comprising at least one non-transitory computer-readable storage medium storing computer instructions that are translatable by a processor to perform: receiving data from one or more data sources via a network; determining an initial value for an item; determining one or more future values for the item, wherein the one or more future values are adjusted based on microeconomic data which is specific to an industry of the item and macroeconomic data which is non-specific to the industry of the item; storing the one or more future values in a data storage device; enabling access to the one or more future values by a user; modifying the one or more future values based on editorial input provided by the user; storing the one or more modified future values in the data storage device; and enabling access by a client device to the one or more modified future values.
 16. The computer program product of claim 15, wherein the computer instructions are further translatable by the processor to perform: receiving and storing competitive set data; and modifying the one or more future values based on the competitive set data.
 17. The computer program product of claim 15, wherein the computer instructions are further translatable by the processor to perform: querying at least one of the one or more data sources using a crawler; and obtaining data from the queried data sources responsive to the queries.
 18. The computer program product of claim 15, wherein the computer instructions are further translatable by the processor to perform: scrubbing the data received from the data sources.
 19. The computer program product of claim 18, wherein the computer instructions are further translatable by the processor to perform: identifying erroneous data; and removing the identified erroneous data from the received data.
 20. The computer program product of claim 15, wherein the computer instructions are further translatable by the processor to perform: determining one or more update intervals; updating the future values at the determined intervals; and storing the updated future values.
 21. The computer program product of claim 15, wherein the computer instructions are further translatable by the processor to perform: enabling a user to access the one or more future values through a workbench module; and modifying the one or more future values based on input provided by the user through the workbench module. 